Overview

Dataset statistics

Number of variables45
Number of observations6687
Missing cells15905
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory360.0 B

Variable types

Categorical35
Numeric10

Alerts

investigacao_aeronave_liberada has constant value "SIM" Constant
investigacao_status has constant value "FINALIZADA" Constant
divulgacao_relatorio_publicado has constant value "SIM" Constant
ocorrencia_cidade has a high cardinality: 344 distinct values High cardinality
ocorrencia_aerodromo has a high cardinality: 152 distinct values High cardinality
ocorrencia_localizacao has a high cardinality: 525 distinct values High cardinality
ocorrencia_tipo has a high cardinality: 58 distinct values High cardinality
ocorrencia_tipo_categoria has a high cardinality: 58 distinct values High cardinality
aeronave_matricula has a high cardinality: 553 distinct values High cardinality
aeronave_modelo has a high cardinality: 204 distinct values High cardinality
aeronave_tipo_icao has a high cardinality: 113 distinct values High cardinality
aeronave_voo_origem has a high cardinality: 218 distinct values High cardinality
aeronave_voo_destino has a high cardinality: 223 distinct values High cardinality
fator_nome has a high cardinality: 69 distinct values High cardinality
recomendacao_conteudo has a high cardinality: 1117 distinct values High cardinality
aeronave_pmd is highly correlated with aeronave_pmd_categoria and 1 other fieldsHigh correlation
aeronave_pmd_categoria is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
aeronave_assentos is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
aeronave_pmd is highly correlated with aeronave_pmd_categoria and 1 other fieldsHigh correlation
aeronave_pmd_categoria is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
aeronave_assentos is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
aeronave_pmd is highly correlated with aeronave_pmd_categoria and 1 other fieldsHigh correlation
aeronave_pmd_categoria is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
aeronave_assentos is highly correlated with aeronave_pmd and 1 other fieldsHigh correlation
fator_nome is highly correlated with fator_condicionante and 6 other fieldsHigh correlation
recomendacao_status is highly correlated with investigacao_aeronave_liberada and 3 other fieldsHigh correlation
fator_condicionante is highly correlated with fator_nome and 6 other fieldsHigh correlation
aeronave_fase_operacao is highly correlated with investigacao_aeronave_liberada and 5 other fieldsHigh correlation
total_aeronaves_envolvidas is highly correlated with fator_nome and 8 other fieldsHigh correlation
aeronave_tipo_veiculo is highly correlated with aeronave_registro_segmento and 8 other fieldsHigh correlation
ocorrencia_tipo is highly correlated with total_aeronaves_envolvidas and 9 other fieldsHigh correlation
ocorrencia_tipo_categoria is highly correlated with total_aeronaves_envolvidas and 9 other fieldsHigh correlation
aeronave_registro_segmento is highly correlated with aeronave_tipo_veiculo and 9 other fieldsHigh correlation
aeronave_nivel_dano is highly correlated with ocorrencia_tipo and 8 other fieldsHigh correlation
fator_aspecto is highly correlated with fator_nome and 5 other fieldsHigh correlation
investigacao_aeronave_liberada is highly correlated with fator_nome and 24 other fieldsHigh correlation
aeronave_registro_categoria is highly correlated with aeronave_tipo_veiculo and 8 other fieldsHigh correlation
ocorrencia_classificacao is highly correlated with aeronave_fase_operacao and 9 other fieldsHigh correlation
aeronave_operador_categoria is highly correlated with recomendacao_status and 21 other fieldsHigh correlation
divulgacao_relatorio_publicado is highly correlated with fator_nome and 24 other fieldsHigh correlation
recomendacao_destinatario_sigla is highly correlated with investigacao_aeronave_liberada and 3 other fieldsHigh correlation
ocorrencia_saida_pista is highly correlated with aeronave_fase_operacao and 7 other fieldsHigh correlation
aeronave_motor_quantidade is highly correlated with aeronave_tipo_veiculo and 8 other fieldsHigh correlation
taxonomia_tipo_icao is highly correlated with total_aeronaves_envolvidas and 9 other fieldsHigh correlation
investigacao_status is highly correlated with fator_nome and 24 other fieldsHigh correlation
aeronave_tipo_operacao is highly correlated with aeronave_registro_segmento and 6 other fieldsHigh correlation
fator_area is highly correlated with fator_nome and 5 other fieldsHigh correlation
aeronave_fabricante is highly correlated with aeronave_tipo_veiculo and 11 other fieldsHigh correlation
ocorrencia_uf is highly correlated with total_aeronaves_envolvidas and 4 other fieldsHigh correlation
aeronave_motor_tipo is highly correlated with aeronave_tipo_veiculo and 9 other fieldsHigh correlation
ocorrencia_classificacao is highly correlated with ocorrencia_uf and 18 other fieldsHigh correlation
ocorrencia_uf is highly correlated with ocorrencia_classificacao and 27 other fieldsHigh correlation
ocorrencia_dia is highly correlated with ocorrencia_uf and 15 other fieldsHigh correlation
ocorrencia_hora is highly correlated with ocorrencia_classificacao and 15 other fieldsHigh correlation
total_recomendacoes is highly correlated with ocorrencia_classificacao and 21 other fieldsHigh correlation
total_aeronaves_envolvidas is highly correlated with ocorrencia_uf and 11 other fieldsHigh correlation
ocorrencia_saida_pista is highly correlated with ocorrencia_tipo and 3 other fieldsHigh correlation
ocorrencia_mes is highly correlated with ocorrencia_uf and 15 other fieldsHigh correlation
ocorrencia_tipo is highly correlated with ocorrencia_classificacao and 31 other fieldsHigh correlation
ocorrencia_tipo_categoria is highly correlated with ocorrencia_classificacao and 31 other fieldsHigh correlation
taxonomia_tipo_icao is highly correlated with ocorrencia_classificacao and 27 other fieldsHigh correlation
aeronave_operador_categoria is highly correlated with ocorrencia_uf and 16 other fieldsHigh correlation
aeronave_tipo_veiculo is highly correlated with ocorrencia_uf and 10 other fieldsHigh correlation
aeronave_fabricante is highly correlated with ocorrencia_classificacao and 27 other fieldsHigh correlation
aeronave_motor_tipo is highly correlated with ocorrencia_uf and 19 other fieldsHigh correlation
aeronave_motor_quantidade is highly correlated with ocorrencia_classificacao and 13 other fieldsHigh correlation
aeronave_pmd is highly correlated with ocorrencia_classificacao and 17 other fieldsHigh correlation
aeronave_pmd_categoria is highly correlated with ocorrencia_classificacao and 17 other fieldsHigh correlation
aeronave_assentos is highly correlated with ocorrencia_classificacao and 15 other fieldsHigh correlation
aeronave_ano_fabricacao is highly correlated with ocorrencia_uf and 14 other fieldsHigh correlation
aeronave_registro_categoria is highly correlated with ocorrencia_uf and 10 other fieldsHigh correlation
aeronave_registro_segmento is highly correlated with ocorrencia_classificacao and 20 other fieldsHigh correlation
aeronave_fase_operacao is highly correlated with ocorrencia_classificacao and 27 other fieldsHigh correlation
aeronave_tipo_operacao is highly correlated with ocorrencia_classificacao and 21 other fieldsHigh correlation
aeronave_nivel_dano is highly correlated with ocorrencia_classificacao and 13 other fieldsHigh correlation
aeronave_fatalidades_total is highly correlated with ocorrencia_uf and 15 other fieldsHigh correlation
fator_nome is highly correlated with ocorrencia_classificacao and 24 other fieldsHigh correlation
fator_aspecto is highly correlated with ocorrencia_tipo and 4 other fieldsHigh correlation
fator_condicionante is highly correlated with ocorrencia_classificacao and 8 other fieldsHigh correlation
fator_area is highly correlated with ocorrencia_tipo and 4 other fieldsHigh correlation
recomendacao_status is highly correlated with ocorrencia_uf and 5 other fieldsHigh correlation
recomendacao_destinatario_sigla is highly correlated with ocorrencia_classificacao and 10 other fieldsHigh correlation
recomendacao_dias_para_feedback is highly correlated with ocorrencia_classificacao and 14 other fieldsHigh correlation
ocorrencia_aerodromo has 3399 (50.8%) missing values Missing
investigacao_aeronave_liberada has 1519 (22.7%) missing values Missing
ocorrencia_localizacao has 149 (2.2%) missing values Missing
aeronave_operador_categoria has 6674 (99.8%) missing values Missing
aeronave_ano_fabricacao has 328 (4.9%) missing values Missing
aeronave_registro_segmento has 153 (2.3%) missing values Missing
aeronave_voo_origem has 68 (1.0%) missing values Missing
fator_condicionante has 734 (11.0%) missing values Missing
recomendacao_dias_para_feedback has 2338 (35.0%) missing values Missing
ocorrencia_hora has 237 (3.5%) zeros Zeros
aeronave_assentos has 305 (4.6%) zeros Zeros
aeronave_fatalidades_total has 4611 (69.0%) zeros Zeros

Reproduction

Analysis started2022-05-29 13:28:25.730062
Analysis finished2022-05-29 13:28:45.203845
Duration19.47 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

ocorrencia_classificacao
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
ACIDENTE
4985 
INCIDENTE GRAVE
1458 
INCIDENTE
 
244

Length

Max length15
Median length8
Mean length9.562733662
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACIDENTE
2nd rowACIDENTE
3rd rowACIDENTE
4th rowACIDENTE
5th rowACIDENTE

Common Values

ValueCountFrequency (%)
ACIDENTE4985
74.5%
INCIDENTE GRAVE1458
 
21.8%
INCIDENTE244
 
3.6%

Length

2022-05-29T10:28:45.266837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:45.331317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
acidente4985
61.2%
incidente1702
 
20.9%
grave1458
 
17.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_cidade
Categorical

HIGH CARDINALITY

Distinct344
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
BRASÍLIA - DF
 
472
RIO DE JANEIRO - RJ
 
265
MANAUS - AM
 
220
SANTOS - SP
 
208
CAUCAIA - CE
 
166
Other values (339)
5356 

Length

Max length37
Median length13
Mean length14.6143263
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.3%

Sample

1st rowGUARULHOS - SP
2nd rowGUARULHOS - SP
3rd rowGUARULHOS - SP
4th rowGUARULHOS - SP
5th rowGUARULHOS - SP

Common Values

ValueCountFrequency (%)
BRASÍLIA - DF472
 
7.1%
RIO DE JANEIRO - RJ265
 
4.0%
MANAUS - AM220
 
3.3%
SANTOS - SP208
 
3.1%
CAUCAIA - CE166
 
2.5%
RECIFE - PE120
 
1.8%
ALMIRANTE TAMANDARÉ - PR117
 
1.7%
GOIÂNIA - GO109
 
1.6%
ITÁPOLIS - SP109
 
1.6%
SÃO PAULO - SP94
 
1.4%
Other values (334)4807
71.9%

Length

2022-05-29T10:28:45.420154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6687
28.6%
sp1212
 
5.2%
am517
 
2.2%
rs510
 
2.2%
pa498
 
2.1%
brasília472
 
2.0%
df472
 
2.0%
go444
 
1.9%
pr428
 
1.8%
do425
 
1.8%
Other values (422)11751
50.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_uf
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
SP
1212 
AM
517 
RS
510 
PA
498 
DF
472 
Other values (22)
3478 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP1212
18.1%
AM517
 
7.7%
RS510
 
7.6%
PA498
 
7.4%
DF472
 
7.1%
GO444
 
6.6%
PR428
 
6.4%
RJ381
 
5.7%
BA337
 
5.0%
MT321
 
4.8%
Other values (17)1567
23.4%

Length

2022-05-29T10:28:45.516873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp1212
18.1%
am517
 
7.7%
rs510
 
7.6%
pa498
 
7.4%
df472
 
7.1%
go444
 
6.6%
pr428
 
6.4%
rj381
 
5.7%
ba337
 
5.0%
mt321
 
4.8%
Other values (17)1567
23.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_aerodromo
Categorical

HIGH CARDINALITY
MISSING

Distinct152
Distinct (%)4.6%
Missing3399
Missing (%)50.8%
Memory size52.4 KiB
SBBR
462 
SBGL
 
124
SBEG
 
110
SDIO
 
101
SBGO
 
94
Other values (147)
2397 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.2%

Sample

1st rowSBGR
2nd rowSBGR
3rd rowSBGR
4th rowSBGR
5th rowSBGR

Common Values

ValueCountFrequency (%)
SBBR462
 
6.9%
SBGL124
 
1.9%
SBEG110
 
1.6%
SDIO101
 
1.5%
SBGO94
 
1.4%
SWUZ82
 
1.2%
SBTE80
 
1.2%
SBRF72
 
1.1%
SBGR68
 
1.0%
SWCA64
 
1.0%
Other values (142)2031
30.4%
(Missing)3399
50.8%

Length

2022-05-29T10:28:45.604261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sbbr462
 
14.1%
sbgl124
 
3.8%
sbeg110
 
3.3%
sdio101
 
3.1%
sbgo94
 
2.9%
swuz82
 
2.5%
sbte80
 
2.4%
sbrf72
 
2.2%
sbgr68
 
2.1%
swca64
 
1.9%
Other values (142)2031
61.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_dia
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.42365784
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2022-05-29T10:28:45.778357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median13
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.267235822
Coefficient of variation (CV)0.6425024722
Kurtosis-1.286524171
Mean14.42365784
Median Absolute Deviation (MAD)9
Skewness0.1739751342
Sum96451
Variance85.88165979
MonotonicityNot monotonic
2022-05-29T10:28:45.867636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4513
 
7.7%
13459
 
6.9%
2354
 
5.3%
22325
 
4.9%
1306
 
4.6%
7300
 
4.5%
26292
 
4.4%
11278
 
4.2%
5273
 
4.1%
3272
 
4.1%
Other values (21)3315
49.6%
ValueCountFrequency (%)
1306
4.6%
2354
5.3%
3272
4.1%
4513
7.7%
5273
4.1%
6138
 
2.1%
7300
4.5%
895
 
1.4%
9160
 
2.4%
10138
 
2.1%
ValueCountFrequency (%)
3183
 
1.2%
30247
3.7%
29140
2.1%
28232
3.5%
27263
3.9%
26292
4.4%
2567
 
1.0%
2498
 
1.5%
23208
3.1%
22325
4.9%

ocorrencia_hora
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.52430088
Minimum0
Maximum23
Zeros237
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2022-05-29T10:28:45.963457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median15
Q319
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.106139388
Coefficient of variation (CV)0.3515583593
Kurtosis0.9513686045
Mean14.52430088
Median Absolute Deviation (MAD)3
Skewness-0.927564702
Sum97124
Variance26.07265945
MonotonicityNot monotonic
2022-05-29T10:28:46.046784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
19797
11.9%
12746
11.2%
13646
9.7%
15482
 
7.2%
17459
 
6.9%
21452
 
6.8%
20447
 
6.7%
11423
 
6.3%
18389
 
5.8%
14385
 
5.8%
Other values (11)1461
21.8%
ValueCountFrequency (%)
0237
 
3.5%
150
 
0.7%
298
 
1.5%
35
 
0.1%
426
 
0.4%
8120
 
1.8%
9174
 
2.6%
10322
4.8%
11423
6.3%
12746
11.2%
ValueCountFrequency (%)
2372
 
1.1%
22107
 
1.6%
21452
6.8%
20447
6.7%
19797
11.9%
18389
5.8%
17459
6.9%
16250
 
3.7%
15482
7.2%
14385
5.8%

investigacao_aeronave_liberada
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1519
Missing (%)22.7%
Memory size52.4 KiB
SIM
5168 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSIM
2nd rowSIM
3rd rowSIM
4th rowSIM
5th rowSIM

Common Values

ValueCountFrequency (%)
SIM5168
77.3%
(Missing)1519
 
22.7%

Length

2022-05-29T10:28:46.134177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:46.189728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sim5168
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

investigacao_status
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
FINALIZADA
6687 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFINALIZADA
2nd rowFINALIZADA
3rd rowFINALIZADA
4th rowFINALIZADA
5th rowFINALIZADA

Common Values

ValueCountFrequency (%)
FINALIZADA6687
100.0%

Length

2022-05-29T10:28:46.246800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:46.304304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
finalizada6687
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

divulgacao_relatorio_publicado
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
SIM
6687 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSIM
2nd rowSIM
3rd rowSIM
4th rowSIM
5th rowSIM

Common Values

ValueCountFrequency (%)
SIM6687
100.0%

Length

2022-05-29T10:28:46.360849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:46.417393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sim6687
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

total_recomendacoes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.115597428
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2022-05-29T10:28:46.458318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile9
Maximum13
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.993402937
Coefficient of variation (CV)0.7273313265
Kurtosis0.8228763435
Mean4.115597428
Median Absolute Deviation (MAD)1
Skewness1.221030728
Sum27521
Variance8.960461141
MonotonicityNot monotonic
2022-05-29T10:28:46.541647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
21566
23.4%
31209
18.1%
11010
15.1%
4848
12.7%
9549
 
8.2%
6486
 
7.3%
7266
 
4.0%
5245
 
3.7%
13208
 
3.1%
8200
 
3.0%
Other values (2)100
 
1.5%
ValueCountFrequency (%)
11010
15.1%
21566
23.4%
31209
18.1%
4848
12.7%
5245
 
3.7%
6486
 
7.3%
7266
 
4.0%
8200
 
3.0%
9549
 
8.2%
1188
 
1.3%
ValueCountFrequency (%)
13208
 
3.1%
1212
 
0.2%
1188
 
1.3%
9549
8.2%
8200
 
3.0%
7266
 
4.0%
6486
7.3%
5245
 
3.7%
4848
12.7%
31209
18.1%

total_aeronaves_envolvidas
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
1
6157 
2
 
530

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
16157
92.1%
2530
 
7.9%

Length

2022-05-29T10:28:46.637375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:46.694911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
16157
92.1%
2530
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_saida_pista
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
NÃO
5569 
SIM
1118 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNÃO
2nd rowNÃO
3rd rowNÃO
4th rowNÃO
5th rowNÃO

Common Values

ValueCountFrequency (%)
NÃO5569
83.3%
SIM1118
 
16.7%

Length

2022-05-29T10:28:46.758399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:46.815935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
não5569
83.3%
sim1118
 
16.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_localizacao
Categorical

HIGH CARDINALITY
MISSING

Distinct525
Distinct (%)8.0%
Missing149
Missing (%)2.2%
Memory size52.4 KiB
-158.711.111.11 / -479.186.111.11
 
234
-23.9597222222 / -46.3269444444
 
208
-3.7325 / -38.7119444444
 
136
\t-25.25833333\t / \t-49.32805556\t
 
117
-22.81 / -43.2505555556
 
96
Other values (520)
5747 

Length

Max length35
Median length31
Mean length29.3796268
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)0.6%

Sample

1st row-23.4355555556 / -46.4730555556
2nd row-23.4355555556 / -46.4730555556
3rd row-23.4355555556 / -46.4730555556
4th row-23.4355555556 / -46.4730555556
5th row-23.4355555556 / -46.4730555556

Common Values

ValueCountFrequency (%)
-158.711.111.11 / -479.186.111.11234
 
3.5%
-23.9597222222 / -46.3269444444208
 
3.1%
-3.7325 / -38.7119444444136
 
2.0%
\t-25.25833333\t / \t-49.32805556\t117
 
1.7%
-22.81 / -43.250555555696
 
1.4%
-215.997.222.22 / -488.327.777.7796
 
1.4%
-30.383.333.333 / -600.62590
 
1.3%
-11.8961111111 / -44.294444444490
 
1.3%
-15.8691666667 / -47.920833333388
 
1.3%
-5.0605555556 / -42.824444444480
 
1.2%
Other values (515)5303
79.3%
(Missing)149
 
2.2%

Length

2022-05-29T10:28:46.898271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6538
33.3%
158.711.111.11234
 
1.2%
479.186.111.11234
 
1.2%
23.9597222222208
 
1.1%
46.3269444444208
 
1.1%
3.7325136
 
0.7%
38.7119444444136
 
0.7%
22.81120
 
0.6%
t-25.25833333\t117
 
0.6%
t-49.32805556\t117
 
0.6%
Other values (1024)11566
59.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_mes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.696724989
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2022-05-29T10:28:46.998463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.369172732
Coefficient of variation (CV)0.5031075246
Kurtosis-1.104060526
Mean6.696724989
Median Absolute Deviation (MAD)3
Skewness-0.07946093492
Sum44781
Variance11.3513249
MonotonicityNot monotonic
2022-05-29T10:28:47.077327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8767
11.5%
9732
10.9%
6729
10.9%
4650
9.7%
12629
9.4%
2536
8.0%
11525
7.9%
1496
7.4%
5445
6.7%
7445
6.7%
Other values (2)733
11.0%
ValueCountFrequency (%)
1496
7.4%
2536
8.0%
3354
5.3%
4650
9.7%
5445
6.7%
6729
10.9%
7445
6.7%
8767
11.5%
9732
10.9%
10379
5.7%
ValueCountFrequency (%)
12629
9.4%
11525
7.9%
10379
5.7%
9732
10.9%
8767
11.5%
7445
6.7%
6729
10.9%
5445
6.7%
4650
9.7%
3354
5.3%

ocorrencia_tipo
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
PERDA DE CONTROLE EM VOO
1103 
FALHA DO MOTOR EM VOO
620 
PERDA DE CONTROLE NO SOLO
588 
COLISÃO COM OBSTÁCULO DURANTE A DECOLAGEM E POUSO
521 
EXCURSÃO DE PISTA
420 
Other values (53)
3435 

Length

Max length50
Median length24
Mean length23.46463287
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCOM PESSOAL EM VOO
2nd rowCOM PESSOAL EM VOO
3rd rowCOM PESSOAL EM VOO
4th rowCOM PESSOAL EM VOO
5th rowCOM PESSOAL EM VOO

Common Values

ValueCountFrequency (%)
PERDA DE CONTROLE EM VOO1103
16.5%
FALHA DO MOTOR EM VOO620
 
9.3%
PERDA DE CONTROLE NO SOLO588
 
8.8%
COLISÃO COM OBSTÁCULO DURANTE A DECOLAGEM E POUSO521
 
7.8%
EXCURSÃO DE PISTA420
 
6.3%
PANE SECA331
 
4.9%
OUTROS305
 
4.6%
COLISÃO COM OBSTÁCULOS NO SOLO277
 
4.1%
INCURSÃO EM PISTA252
 
3.8%
OPERAÇÃO A BAIXA ALTITUDE232
 
3.5%
Other values (48)2038
30.5%

Length

2022-05-29T10:28:47.179999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de2735
 
9.5%
em2211
 
7.7%
voo2113
 
7.3%
perda1729
 
6.0%
controle1691
 
5.9%
com1249
 
4.3%
pouso1101
 
3.8%
no946
 
3.3%
solo946
 
3.3%
colisão935
 
3.2%
Other values (97)13183
45.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ocorrencia_tipo_categoria
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
PERDA DE CONTROLE EM VOO
1103 
FALHA OU MAU FUNCIONAMENTO DO MOTOR | FALHA DO MOTOR EM VOO
620 
PERDA DE CONTROLE NO SOLO
588 
COLISÃO COM OBSTÁCULO DURANTE A DECOLAGEM E POUSO
521 
EXCURSÃO DE PISTA
420 
Other values (53)
3435 

Length

Max length94
Median length25
Mean length34.51577688
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowOUTROS | COM PESSOAL EM VOO
2nd rowOUTROS | COM PESSOAL EM VOO
3rd rowOUTROS | COM PESSOAL EM VOO
4th rowOUTROS | COM PESSOAL EM VOO
5th rowOUTROS | COM PESSOAL EM VOO

Common Values

ValueCountFrequency (%)
PERDA DE CONTROLE EM VOO1103
16.5%
FALHA OU MAU FUNCIONAMENTO DO MOTOR | FALHA DO MOTOR EM VOO620
 
9.3%
PERDA DE CONTROLE NO SOLO588
 
8.8%
COLISÃO COM OBSTÁCULO DURANTE A DECOLAGEM E POUSO521
 
7.8%
EXCURSÃO DE PISTA420
 
6.3%
COMBUSTÍVEL | PANE SECA331
 
4.9%
OUTROS305
 
4.6%
COLISÃO NO SOLO | COLISÃO COM OBSTÁCULOS NO SOLO277
 
4.1%
INCURSÃO EM PISTA252
 
3.8%
OPERAÇÃO A BAIXA ALTITUDE232
 
3.5%
Other values (48)2038
30.5%

Length

2022-05-29T10:28:47.290606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3398
 
8.0%
de3393
 
8.0%
em2427
 
5.7%
voo2329
 
5.5%
perda1945
 
4.6%
falha1861
 
4.4%
controle1691
 
4.0%
colisão1428
 
3.3%
com1383
 
3.2%
motor1301
 
3.1%
Other values (97)21487
50.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

taxonomia_tipo_icao
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
LOC-I
1103 
OTHR
700 
SCF-PP
673 
LOC-G
588 
RE
572 
Other values (22)
3051 

Length

Max length6
Median length4
Mean length4.308658591
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHR
2nd rowOTHR
3rd rowOTHR
4th rowOTHR
5th rowOTHR

Common Values

ValueCountFrequency (%)
LOC-I1103
16.5%
OTHR700
10.5%
SCF-PP673
10.1%
LOC-G588
8.8%
RE572
8.6%
CTOL521
7.8%
SCF-NP515
7.7%
FUEL349
 
5.2%
GCOL277
 
4.1%
RI252
 
3.8%
Other values (17)1137
17.0%

Length

2022-05-29T10:28:47.397742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loc-i1103
16.5%
othr700
10.5%
scf-pp673
10.1%
loc-g588
8.8%
re572
8.6%
ctol521
7.8%
scf-np515
7.7%
fuel349
 
5.2%
gcol277
 
4.1%
ri252
 
3.8%
Other values (17)1137
17.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_matricula
Categorical

HIGH CARDINALITY

Distinct553
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
PRAFA
 
208
PREES
 
136
PRGTN
 
117
PTMFW
 
117
FAB2345
 
117
Other values (548)
5992 

Length

Max length7
Median length5
Mean length5.051144011
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)0.6%

Sample

1st rowPRTKB
2nd rowPRTKB
3rd rowPRTKB
4th rowPRTKB
5th rowPRTKB

Common Values

ValueCountFrequency (%)
PRAFA208
 
3.1%
PREES136
 
2.0%
PRGTN117
 
1.7%
PTMFW117
 
1.7%
FAB2345117
 
1.7%
YV293790
 
1.3%
PPELA90
 
1.3%
CSTOF88
 
1.3%
PTCNL78
 
1.2%
PRMBG78
 
1.2%
Other values (543)5568
83.3%

Length

2022-05-29T10:28:47.500415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prafa208
 
3.1%
prees136
 
2.0%
prgtn117
 
1.7%
ptmfw117
 
1.7%
fab2345117
 
1.7%
yv293790
 
1.3%
ppela90
 
1.3%
cstof88
 
1.3%
ptcnl78
 
1.2%
prmbg78
 
1.2%
Other values (543)5568
83.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_operador_categoria
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)23.1%
Missing6674
Missing (%)99.8%
Memory size52.4 KiB
PARTICULAR
INSTRUÇÃO
AGRÍCOLA

Length

Max length10
Median length9
Mean length9.076923077
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGRÍCOLA
2nd rowAGRÍCOLA
3rd rowAGRÍCOLA
4th rowAGRÍCOLA
5th rowPARTICULAR

Common Values

ValueCountFrequency (%)
PARTICULAR5
 
0.1%
INSTRUÇÃO4
 
0.1%
AGRÍCOLA4
 
0.1%
(Missing)6674
99.8%

Length

2022-05-29T10:28:47.603087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:47.671534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
particular5
38.5%
instrução4
30.8%
agrícola4
30.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_tipo_veiculo
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing38
Missing (%)0.6%
Memory size52.4 KiB
AVIÃO
4998 
HELICÓPTERO
1449 
ULTRALEVE
 
107
PLANADOR
 
74
ANFÍBIO
 
21

Length

Max length11
Median length5
Mean length6.411640848
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAVIÃO
2nd rowAVIÃO
3rd rowAVIÃO
4th rowAVIÃO
5th rowAVIÃO

Common Values

ValueCountFrequency (%)
AVIÃO4998
74.7%
HELICÓPTERO1449
 
21.7%
ULTRALEVE107
 
1.6%
PLANADOR74
 
1.1%
ANFÍBIO21
 
0.3%
(Missing)38
 
0.6%

Length

2022-05-29T10:28:47.750330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:47.909050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
avião4998
75.2%
helicóptero1449
 
21.8%
ultraleve107
 
1.6%
planador74
 
1.1%
anfíbio21
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_fabricante
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)0.7%
Missing61
Missing (%)0.9%
Memory size52.4 KiB
CESSNA AIRCRAFT
1466 
NEIVA INDUSTRIA AERONAUTICA
809 
PIPER AIRCRAFT
505 
EMBRAER
482 
BELL HELICOPTER
372 
Other values (40)
2992 

Length

Max length35
Median length15
Mean length16.00120736
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowAEROSPATIALE AND ALENIA
2nd rowAEROSPATIALE AND ALENIA
3rd rowAEROSPATIALE AND ALENIA
4th rowAEROSPATIALE AND ALENIA
5th rowAEROSPATIALE AND ALENIA

Common Values

ValueCountFrequency (%)
CESSNA AIRCRAFT1466
21.9%
NEIVA INDUSTRIA AERONAUTICA809
12.1%
PIPER AIRCRAFT505
 
7.6%
EMBRAER482
 
7.2%
BELL HELICOPTER372
 
5.6%
BOEING COMPANY368
 
5.5%
EUROCOPTER FRANCE337
 
5.0%
AIRBUS INDUSTRIE300
 
4.5%
BEECH AIRCRAFT273
 
4.1%
AERO BOERO236
 
3.5%
Other values (35)1478
22.1%

Length

2022-05-29T10:28:48.019862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aircraft2332
17.2%
cessna1466
 
10.8%
aeronautica851
 
6.3%
industria850
 
6.3%
neiva809
 
6.0%
helicopter677
 
5.0%
piper505
 
3.7%
eurocopter496
 
3.7%
embraer482
 
3.5%
bell372
 
2.7%
Other values (61)4748
34.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_modelo
Categorical

HIGH CARDINALITY

Distinct204
Distinct (%)3.1%
Missing34
Missing (%)0.5%
Memory size52.4 KiB
737-8EH
 
222
560XLS+
 
208
AB-115
 
206
AS 350 B2
 
205
EMB-202
 
197
Other values (199)
5615 

Length

Max length15
Median length7
Mean length6.752442507
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.2%

Sample

1st rowATR-42-500
2nd rowATR-42-500
3rd rowATR-42-500
4th rowATR-42-500
5th rowATR-42-500

Common Values

ValueCountFrequency (%)
737-8EH222
 
3.3%
560XLS+208
 
3.1%
AB-115206
 
3.1%
AS 350 B2205
 
3.1%
EMB-202197
 
2.9%
EMB-201A192
 
2.9%
AT-502B163
 
2.4%
AS 350 BA155
 
2.3%
BK117 C-2136
 
2.0%
EMB-202A123
 
1.8%
Other values (194)4846
72.5%

Length

2022-05-29T10:28:48.131273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
as404
 
4.8%
350364
 
4.4%
737-8eh222
 
2.7%
560xls208
 
2.5%
ab-115206
 
2.5%
b2205
 
2.5%
emb-202197
 
2.4%
emb-201a192
 
2.3%
at-502b163
 
2.0%
r44162
 
1.9%
Other values (217)6034
72.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_tipo_icao
Categorical

HIGH CARDINALITY

Distinct113
Distinct (%)1.7%
Missing57
Missing (%)0.9%
Memory size52.4 KiB
IPAN
540 
AS50
 
382
B06
 
270
B738
 
222
C56X
 
208
Other values (108)
5008 

Length

Max length4
Median length4
Mean length3.904374057
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowAT45
2nd rowAT45
3rd rowAT45
4th rowAT45
5th rowAT45

Common Values

ValueCountFrequency (%)
IPAN540
 
8.1%
AS50382
 
5.7%
B06270
 
4.0%
B738222
 
3.3%
C56X208
 
3.1%
AB11206
 
3.1%
C172199
 
3.0%
C208180
 
2.7%
A320178
 
2.7%
AT5T163
 
2.4%
Other values (103)4082
61.0%

Length

2022-05-29T10:28:48.236426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ipan540
 
8.1%
as50382
 
5.8%
b06270
 
4.1%
b738222
 
3.3%
c56x208
 
3.1%
ab11206
 
3.1%
c172199
 
3.0%
c208180
 
2.7%
a320178
 
2.7%
at5t163
 
2.5%
Other values (103)4082
61.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_motor_tipo
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing51
Missing (%)0.8%
Memory size52.4 KiB
PISTÃO
3091 
TURBOEIXO
1260 
JATO
1209 
TURBOÉLICE
1002 
SEM TRAÇÃO
 
74

Length

Max length10
Median length6
Mean length6.853827607
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTURBOÉLICE
2nd rowTURBOÉLICE
3rd rowTURBOÉLICE
4th rowTURBOÉLICE
5th rowTURBOÉLICE

Common Values

ValueCountFrequency (%)
PISTÃO3091
46.2%
TURBOEIXO1260
18.8%
JATO1209
 
18.1%
TURBOÉLICE1002
 
15.0%
SEM TRAÇÃO74
 
1.1%
(Missing)51
 
0.8%

Length

2022-05-29T10:28:48.339594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:48.403578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
pistão3091
46.1%
turboeixo1260
18.8%
jato1209
 
18.0%
turboélice1002
 
14.9%
sem74
 
1.1%
tração74
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_motor_quantidade
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing21
Missing (%)0.3%
Memory size52.4 KiB
MONOMOTOR
3910 
BIMOTOR
2649 
SEM TRAÇÃO
 
107

Length

Max length10
Median length9
Mean length8.221272127
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBIMOTOR
2nd rowBIMOTOR
3rd rowBIMOTOR
4th rowBIMOTOR
5th rowBIMOTOR

Common Values

ValueCountFrequency (%)
MONOMOTOR3910
58.5%
BIMOTOR2649
39.6%
SEM TRAÇÃO107
 
1.6%
(Missing)21
 
0.3%

Length

2022-05-29T10:28:48.500298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:48.568746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
monomotor3910
57.7%
bimotor2649
39.1%
sem107
 
1.6%
tração107
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_pmd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct150
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14482.44235
Minimum0
Maximum346544
Zeros54
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2022-05-29T10:28:48.650796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile660
Q11315
median2100
Q34750
95-th percentile70533
Maximum346544
Range346544
Interquartile range (IQR)3435

Descriptive statistics

Standard deviation42787.91928
Coefficient of variation (CV)2.954468469
Kurtosis30.35580511
Mean14482.44235
Median Absolute Deviation (MAD)1330
Skewness5.17573369
Sum96844092
Variance1830806036
MonotonicityNot monotonic
2022-05-29T10:28:48.759392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800536
 
8.0%
3629333
 
5.0%
70533222
 
3.3%
2250209
 
3.1%
9163208
 
3.1%
770206
 
3.1%
1633183
 
2.7%
2100155
 
2.3%
1134154
 
2.3%
1315150
 
2.2%
Other values (140)4331
64.8%
ValueCountFrequency (%)
054
0.8%
2804
 
0.1%
3904
 
0.1%
3972
 
< 0.1%
43018
 
0.3%
4503
 
< 0.1%
5208
 
0.1%
55010
 
0.1%
5622
 
< 0.1%
56560
0.9%
ValueCountFrequency (%)
34654441
 
0.6%
2370004
 
0.1%
230000108
1.6%
8900010
 
0.1%
7800024
 
0.4%
7700066
 
1.0%
7550078
 
1.2%
70533222
3.3%
7000010
 
0.1%
6803812
 
0.2%

aeronave_pmd_categoria
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct150
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14482.44235
Minimum0
Maximum346544
Zeros54
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2022-05-29T10:28:48.877906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile660
Q11315
median2100
Q34750
95-th percentile70533
Maximum346544
Range346544
Interquartile range (IQR)3435

Descriptive statistics

Standard deviation42787.91928
Coefficient of variation (CV)2.954468469
Kurtosis30.35580511
Mean14482.44235
Median Absolute Deviation (MAD)1330
Skewness5.17573369
Sum96844092
Variance1830806036
MonotonicityNot monotonic
2022-05-29T10:28:48.987545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800536
 
8.0%
3629333
 
5.0%
70533222
 
3.3%
2250209
 
3.1%
9163208
 
3.1%
770206
 
3.1%
1633183
 
2.7%
2100155
 
2.3%
1134154
 
2.3%
1315150
 
2.2%
Other values (140)4331
64.8%
ValueCountFrequency (%)
054
0.8%
2804
 
0.1%
3904
 
0.1%
3972
 
< 0.1%
43018
 
0.3%
4503
 
< 0.1%
5208
 
0.1%
55010
 
0.1%
5622
 
< 0.1%
56560
0.9%
ValueCountFrequency (%)
34654441
 
0.6%
2370004
 
0.1%
230000108
1.6%
8900010
 
0.1%
7800024
 
0.4%
7700066
 
1.0%
7550078
 
1.2%
70533222
3.3%
7000010
 
0.1%
6803812
 
0.2%

aeronave_assentos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct31
Distinct (%)0.5%
Missing46
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean22.53064298
Minimum0
Maximum384
Zeros305
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2022-05-29T10:28:49.102121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median6
Q39
95-th percentile183
Maximum384
Range384
Interquartile range (IQR)7

Descriptive statistics

Standard deviation55.42967434
Coefficient of variation (CV)2.460190524
Kurtosis14.02336478
Mean22.53064298
Median Absolute Deviation (MAD)4
Skewness3.585070067
Sum149626
Variance3072.448797
MonotonicityNot monotonic
2022-05-29T10:28:49.199884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
61031
15.4%
11021
15.3%
2757
11.3%
4665
9.9%
7583
8.7%
10329
 
4.9%
0305
 
4.6%
14236
 
3.5%
8235
 
3.5%
5200
 
3.0%
Other values (21)1279
19.1%
ValueCountFrequency (%)
0305
 
4.6%
11021
15.3%
2757
11.3%
3111
 
1.7%
4665
9.9%
5200
 
3.0%
61031
15.4%
7583
8.7%
8235
 
3.5%
9146
 
2.2%
ValueCountFrequency (%)
38441
 
0.6%
24020
 
0.3%
23210
 
0.1%
197138
2.1%
19360
0.9%
18446
 
0.7%
18330
 
0.4%
18278
1.2%
17224
 
0.4%
12525
 
0.4%

aeronave_ano_fabricacao
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct66
Distinct (%)1.0%
Missing328
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean1991.929706
Minimum1945
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2022-05-29T10:28:49.312448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1945
5-th percentile1964
Q11979
median1993
Q32007
95-th percentile2013
Maximum2018
Range73
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.64804176
Coefficient of variation (CV)0.007855719863
Kurtosis-0.532472147
Mean1991.929706
Median Absolute Deviation (MAD)14
Skewness-0.4415478392
Sum12666681
Variance244.8612108
MonotonicityNot monotonic
2022-05-29T10:28:49.420276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2010455
 
6.8%
2001245
 
3.7%
1979237
 
3.5%
1983232
 
3.5%
2007223
 
3.3%
2013221
 
3.3%
2012215
 
3.2%
1974193
 
2.9%
1992190
 
2.8%
1991184
 
2.8%
Other values (56)3964
59.3%
(Missing)328
 
4.9%
ValueCountFrequency (%)
19452
 
< 0.1%
194626
 
0.4%
19472
 
< 0.1%
19482
 
< 0.1%
195010
 
0.1%
195150
0.7%
195328
 
0.4%
195510
 
0.1%
196083
1.2%
196130
 
0.4%
ValueCountFrequency (%)
201814
 
0.2%
201712
 
0.2%
201635
 
0.5%
201540
 
0.6%
201434
 
0.5%
2013221
3.3%
2012215
3.2%
2011127
 
1.9%
2010455
6.8%
2009114
 
1.7%

aeronave_registro_categoria
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing38
Missing (%)0.6%
Memory size52.4 KiB
AVIÃO
4998 
HELICÓPTERO
1449 
ULTRALEVE
 
107
PLANADOR
 
74
ANFÍBIO
 
21

Length

Max length11
Median length5
Mean length6.411640848
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAVIÃO
2nd rowAVIÃO
3rd rowAVIÃO
4th rowAVIÃO
5th rowAVIÃO

Common Values

ValueCountFrequency (%)
AVIÃO4998
74.7%
HELICÓPTERO1449
 
21.7%
ULTRALEVE107
 
1.6%
PLANADOR74
 
1.1%
ANFÍBIO21
 
0.3%
(Missing)38
 
0.6%

Length

2022-05-29T10:28:49.533814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:49.597770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
avião4998
75.2%
helicóptero1449
 
21.8%
ultraleve107
 
1.6%
planador74
 
1.1%
anfíbio21
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_registro_segmento
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct11
Distinct (%)0.2%
Missing153
Missing (%)2.3%
Memory size52.4 KiB
PARTICULAR
1892 
INSTRUÇÃO
1040 
TÁXI AÉREO
848 
REGULAR
818 
AGRÍCOLA
687 
Other values (6)
1249 

Length

Max length22
Median length10
Mean length10.56504438
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREGULAR
2nd rowREGULAR
3rd rowREGULAR
4th rowREGULAR
5th rowREGULAR

Common Values

ValueCountFrequency (%)
PARTICULAR1892
28.3%
INSTRUÇÃO1040
15.6%
TÁXI AÉREO848
12.7%
REGULAR818
12.2%
AGRÍCOLA687
 
10.3%
ADMINISTRAÇÃO DIRETA663
 
9.9%
ESPECIALIZADA241
 
3.6%
EXPERIMENTAL127
 
1.9%
MÚLTIPLA107
 
1.6%
ADMINISTRAÇÃO INDIRETA96
 
1.4%
(Missing)153
 
2.3%

Length

2022-05-29T10:28:49.701960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
particular1892
23.2%
instrução1040
12.8%
táxi848
10.4%
aéreo848
10.4%
regular833
10.2%
administração759
9.3%
agrícola687
 
8.4%
direta663
 
8.1%
especializada241
 
3.0%
experimental127
 
1.6%
Other values (3)218
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_voo_origem
Categorical

HIGH CARDINALITY
MISSING

Distinct218
Distinct (%)3.3%
Missing68
Missing (%)1.0%
Memory size52.4 KiB
FORA DE AERODROMO
1718 
PRESIDENTE JUSCELINO KUBITSCHEK
 
343
SANTOS DUMONT
 
269
PINTO MARTINS
 
156
VAL DE CANS / JÚLIO CEZAR RIBEIRO
 
124
Other values (213)
4009 

Length

Max length40
Median length17
Mean length17.73576069
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.3%

Sample

1st rowFORA DE AERODROMO
2nd rowFORA DE AERODROMO
3rd rowFORA DE AERODROMO
4th rowFORA DE AERODROMO
5th rowFORA DE AERODROMO

Common Values

ValueCountFrequency (%)
FORA DE AERODROMO1718
25.7%
PRESIDENTE JUSCELINO KUBITSCHEK343
 
5.1%
SANTOS DUMONT269
 
4.0%
PINTO MARTINS156
 
2.3%
VAL DE CANS / JÚLIO CEZAR RIBEIRO124
 
1.9%
FLORES118
 
1.8%
SÃO PEDRO117
 
1.7%
BASE AÉREA DE SANTA CRUZ117
 
1.7%
ZUMBI DOS PALMARES111
 
1.7%
AEROCLUBE DE ITÁPOLIS109
 
1.6%
Other values (208)3437
51.4%

Length

2022-05-29T10:28:49.822269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de2528
 
14.0%
fora1718
 
9.5%
aerodromo1718
 
9.5%
presidente357
 
2.0%
juscelino343
 
1.9%
kubitschek343
 
1.9%
312
 
1.7%
santos269
 
1.5%
dumont269
 
1.5%
santa186
 
1.0%
Other values (358)10036
55.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_voo_destino
Categorical

HIGH CARDINALITY

Distinct223
Distinct (%)3.4%
Missing60
Missing (%)0.9%
Memory size52.4 KiB
FORA DE AERODROMO
2043 
BASE AÉREA DE SANTOS
 
208
PRESIDENTE JUSCELINO KUBITSCHEK
 
197
BACACHERI
 
148
EDUARDO GOMES
 
147
Other values (218)
3884 

Length

Max length46
Median length17
Mean length17.11800211
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.2%

Sample

1st rowFORA DE AERODROMO
2nd rowFORA DE AERODROMO
3rd rowFORA DE AERODROMO
4th rowFORA DE AERODROMO
5th rowFORA DE AERODROMO

Common Values

ValueCountFrequency (%)
FORA DE AERODROMO2043
30.6%
BASE AÉREA DE SANTOS208
 
3.1%
PRESIDENTE JUSCELINO KUBITSCHEK197
 
2.9%
BACACHERI148
 
2.2%
EDUARDO GOMES147
 
2.2%
AEROCLUBE DE ITÁPOLIS121
 
1.8%
MARECHAL CUNHA MACHADO120
 
1.8%
ZUMBI DOS PALMARES112
 
1.7%
VAL DE CANS / JÚLIO CEZAR RIBEIRO99
 
1.5%
RONDONÓPOLIS95
 
1.4%
Other values (213)3337
49.9%

Length

2022-05-29T10:28:50.047453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de2910
 
16.4%
aerodromo2053
 
11.6%
fora2043
 
11.5%
300
 
1.7%
santos240
 
1.4%
aeroclube221
 
1.2%
presidente214
 
1.2%
base208
 
1.2%
aérea208
 
1.2%
juscelino197
 
1.1%
Other values (368)9146
51.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_fase_operacao
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
DECOLAGEM
1250 
POUSO
1001 
CRUZEIRO
846 
CORRIDA APÓS POUSO
618 
APROXIMAÇÃO FINAL
525 
Other values (18)
2447 

Length

Max length31
Median length9
Mean length10.49798116
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDESCIDA
2nd rowDESCIDA
3rd rowDESCIDA
4th rowDESCIDA
5th rowDESCIDA

Common Values

ValueCountFrequency (%)
DECOLAGEM1250
18.7%
POUSO1001
15.0%
CRUZEIRO846
12.7%
CORRIDA APÓS POUSO618
9.2%
APROXIMAÇÃO FINAL525
7.9%
MANOBRA404
 
6.0%
CIRCUITO DE TRÁFEGO324
 
4.8%
ESPECIALIZADA245
 
3.7%
PAIRADO210
 
3.1%
SUBIDA210
 
3.1%
Other values (13)1054
15.8%

Length

2022-05-29T10:28:50.151117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pouso1619
16.2%
decolagem1287
12.9%
cruzeiro846
 
8.5%
corrida618
 
6.2%
após618
 
6.2%
final530
 
5.3%
aproximação529
 
5.3%
manobra404
 
4.0%
de351
 
3.5%
circuito324
 
3.2%
Other values (26)2865
28.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_tipo_operacao
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing42
Missing (%)0.6%
Memory size52.4 KiB
PRIVADA
1956 
INSTRUÇÃO
1014 
AGRÍCOLA
960 
TÁXI AÉREO
845 
REGULAR
842 
Other values (4)
1028 

Length

Max length13
Median length8
Mean length8.252671181
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREGULAR
2nd rowREGULAR
3rd rowREGULAR
4th rowREGULAR
5th rowREGULAR

Common Values

ValueCountFrequency (%)
PRIVADA1956
29.3%
INSTRUÇÃO1014
15.2%
AGRÍCOLA960
14.4%
TÁXI AÉREO845
12.6%
REGULAR842
12.6%
POLICIAL626
 
9.4%
ESPECIALIZADA280
 
4.2%
NÃO REGULAR115
 
1.7%
EXPERIMENTAL7
 
0.1%
(Missing)42
 
0.6%

Length

2022-05-29T10:28:50.253789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:50.323229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
privada1956
25.7%
instrução1014
13.3%
agrícola960
12.6%
regular957
12.6%
táxi845
11.1%
aéreo845
11.1%
policial626
 
8.2%
especializada280
 
3.7%
não115
 
1.5%
experimental7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_nivel_dano
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing7
Missing (%)0.1%
Memory size52.4 KiB
SUBSTANCIAL
3221 
DESTRUÍDA
1670 
LEVE
980 
NENHUM
809 

Length

Max length11
Median length9
Mean length8.86751497
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNENHUM
2nd rowNENHUM
3rd rowNENHUM
4th rowNENHUM
5th rowNENHUM

Common Values

ValueCountFrequency (%)
SUBSTANCIAL3221
48.2%
DESTRUÍDA1670
25.0%
LEVE980
 
14.7%
NENHUM809
 
12.1%
(Missing)7
 
0.1%

Length

2022-05-29T10:28:50.460125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:50.527581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
substancial3221
48.2%
destruída1670
25.0%
leve980
 
14.7%
nenhum809
 
12.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aeronave_fatalidades_total
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.012412143
Minimum0
Maximum10
Zeros4611
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2022-05-29T10:28:50.603353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.958512632
Coefficient of variation (CV)1.934501325
Kurtosis4.164042324
Mean1.012412143
Median Absolute Deviation (MAD)0
Skewness2.158567841
Sum6770
Variance3.835771731
MonotonicityNot monotonic
2022-05-29T10:28:50.682684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
04611
69.0%
1672
 
10.0%
4380
 
5.7%
2327
 
4.9%
5262
 
3.9%
7208
 
3.1%
3138
 
2.1%
1039
 
0.6%
827
 
0.4%
623
 
0.3%
ValueCountFrequency (%)
04611
69.0%
1672
 
10.0%
2327
 
4.9%
3138
 
2.1%
4380
 
5.7%
5262
 
3.9%
623
 
0.3%
7208
 
3.1%
827
 
0.4%
1039
 
0.6%
ValueCountFrequency (%)
1039
 
0.6%
827
 
0.4%
7208
 
3.1%
623
 
0.3%
5262
 
3.9%
4380
 
5.7%
3138
 
2.1%
2327
 
4.9%
1672
 
10.0%
04611
69.0%

fator_nome
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct69
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
JULGAMENTO DE PILOTAGEM
630 
SUPERVISÃO GERENCIAL
483 
APLICAÇÃO DE COMANDOS
 
441
PROCESSO DECISÓRIO
 
351
ATITUDE
 
345
Other values (64)
4437 

Length

Max length41
Median length20
Mean length19.03723643
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPLICAÇÃO DE COMANDOS
2nd rowAPLICAÇÃO DE COMANDOS
3rd rowAPLICAÇÃO DE COMANDOS
4th rowATENÇÃO
5th rowATENÇÃO

Common Values

ValueCountFrequency (%)
JULGAMENTO DE PILOTAGEM630
 
9.4%
SUPERVISÃO GERENCIAL483
 
7.2%
APLICAÇÃO DE COMANDOS441
 
6.6%
PROCESSO DECISÓRIO351
 
5.2%
ATITUDE345
 
5.2%
MANUTENÇÃO DA AERONAVE327
 
4.9%
PLANEJAMENTO DE VOO318
 
4.8%
PERCEPÇÃO291
 
4.4%
PROCESSOS ORGANIZACIONAIS208
 
3.1%
SISTEMAS DE APOIO205
 
3.1%
Other values (59)3088
46.2%

Length

2022-05-29T10:28:50.785356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de2329
 
14.4%
julgamento630
 
3.9%
pilotagem630
 
3.9%
gerencial579
 
3.6%
do571
 
3.5%
supervisão512
 
3.2%
voo493
 
3.1%
aplicação441
 
2.7%
comandos441
 
2.7%
planejamento434
 
2.7%
Other values (104)9083
56.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fator_aspecto
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing29
Missing (%)0.4%
Memory size52.4 KiB
DESEMPENHO DO SER HUMANO
3314 
ASPECTO PSICOLÓGICO
2639 
INFRAESTRUTURA AEROPORTUÁRIA
 
177
ELEMENTOS RELACIONADOS AO AMBIENTE OPERACIONAL
 
154
ASPECTO MÉDICO
 
153
Other values (6)
 
221

Length

Max length46
Median length24
Mean length22.08065485
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDESEMPENHO DO SER HUMANO
2nd rowDESEMPENHO DO SER HUMANO
3rd rowDESEMPENHO DO SER HUMANO
4th rowASPECTO PSICOLÓGICO
5th rowASPECTO PSICOLÓGICO

Common Values

ValueCountFrequency (%)
DESEMPENHO DO SER HUMANO3314
49.6%
ASPECTO PSICOLÓGICO2639
39.5%
INFRAESTRUTURA AEROPORTUÁRIA177
 
2.6%
ELEMENTOS RELACIONADOS AO AMBIENTE OPERACIONAL154
 
2.3%
ASPECTO MÉDICO153
 
2.3%
OUTRO88
 
1.3%
ERGONOMIA34
 
0.5%
INFRAESTRUTURA DE TRÁFEGO AÉREO34
 
0.5%
ASPECTO DE FABRICAÇÃO31
 
0.5%
ASPECTO DE PROJETO27
 
0.4%
(Missing)29
 
0.4%

Length

2022-05-29T10:28:50.889184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
do3321
16.3%
desempenho3314
16.2%
ser3314
16.2%
humano3314
16.2%
aspecto2857
14.0%
psicológico2639
12.9%
infraestrutura211
 
1.0%
aeroportuária177
 
0.9%
ao154
 
0.8%
operacional154
 
0.8%
Other values (13)976
 
4.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fator_condicionante
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.1%
Missing734
Missing (%)11.0%
Memory size52.4 KiB
OPERAÇÃO DA AERONAVE
2782 
INDIVIDUAL
1405 
ORGANIZACIONAL
886 
PSICOSSOCIAL
348 
MANUTENÇÃO DA AERONAVE
327 

Length

Max length38
Median length20
Mean length17.00890307
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOPERAÇÃO DA AERONAVE
2nd rowOPERAÇÃO DA AERONAVE
3rd rowOPERAÇÃO DA AERONAVE
4th rowINDIVIDUAL
5th rowINDIVIDUAL

Common Values

ValueCountFrequency (%)
OPERAÇÃO DA AERONAVE2782
41.6%
INDIVIDUAL1405
21.0%
ORGANIZACIONAL886
 
13.2%
PSICOSSOCIAL348
 
5.2%
MANUTENÇÃO DA AERONAVE327
 
4.9%
PRESTAÇÃO DE SERVIÇOS DE TRÁFEGO AÉREO205
 
3.1%
(Missing)734
 
11.0%

Length

2022-05-29T10:28:50.976671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:51.046110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
da3109
23.6%
aeronave3109
23.6%
operação2782
21.1%
individual1405
10.6%
organizacional886
 
6.7%
de410
 
3.1%
psicossocial348
 
2.6%
manutenção327
 
2.5%
prestação205
 
1.6%
serviços205
 
1.6%
Other values (2)410
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

fator_area
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing29
Missing (%)0.4%
Memory size52.4 KiB
FATOR OPERACIONAL
3679 
FATOR HUMANO
2826 
OUTRO
 
88
FATOR MATERIAL
 
65

Length

Max length17
Median length17
Mean length14.6898468
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFATOR OPERACIONAL
2nd rowFATOR OPERACIONAL
3rd rowFATOR OPERACIONAL
4th rowFATOR HUMANO
5th rowFATOR HUMANO

Common Values

ValueCountFrequency (%)
FATOR OPERACIONAL3679
55.0%
FATOR HUMANO2826
42.3%
OUTRO88
 
1.3%
FATOR MATERIAL65
 
1.0%
(Missing)29
 
0.4%

Length

2022-05-29T10:28:51.157710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:51.222686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
fator6570
49.7%
operacional3679
27.8%
humano2826
21.4%
outro88
 
0.7%
material65
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recomendacao_conteudo
Categorical

HIGH CARDINALITY

Distinct1117
Distinct (%)16.7%
Missing2
Missing (%)< 0.1%
Memory size52.4 KiB
Atuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.
 
32
Divulgar o conteúdo do presente relatório durante a realização de seminários, palestras e atividades afins voltadas aos proprietários, operadores e exploradores de aeronaves agrícolas.
 
28
Atuar junto a INFRAMERICA, a fim de que aquele Operador de Aeródromo adote medidas em relação à estrutura de iluminação dos pátios de estacionamento (Píer Norte e Píer Sul), de modo a evitar que a luz proveniente dos respectivos holofotes interfira negativamente na linha de visada dos controladores da TWR-BR.
 
26
ATUAR EM CONJUNTO COM A INFRAMERICA E O DECEA, A FIM DE QUE SEJA AVALIADA A PERTINêNCIA DE SE MODIFICAR UMA OU AMBAS AS LETRAS DESIGNATIVAS DAS TAXIWAYS “C” (CHARLIE) E “G” (GOLF) DO AERóDROMO INTERNACIONAL DE BRASíLIA, DE MODO A EVITAR CONFUSõES OU EQUíVOCOS POR PARTE DAS TRIPULAçõES QUE OPERAM NAQUELE AERóDROMO.
 
26
ATUAR EM CONJUNTO COM A INFRAMERICA E O DECEA, A FIM DE QUE CâMERAS DE USO EXCLUSIVO DO DTCEA-BR SEJAM INSTALADAS NO AERóDROMO INTERNACIONAL DE BRASíLIA, DE MODO A GARANTIR A VISUALIZAçãO E O CONTROLE DE TODOS OS PONTOS CEGOS DAQUELE AERóDROMO.
 
26
Other values (1112)
6547 

Length

Max length626
Median length270
Mean length277.3464473
Min length68

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)1.7%

Sample

1st rowAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.
2nd rowAtuar junto à Administração do Aeroporto Internacional de Guarulhos, de forma que este passe a ministrar treinamento teórico e prático de atendimento às vítimas de acidentes envolvendo os principais tipos de aeronaves que operam naquela localidade, principalmente os das Linhas Aéreas Regulares, com especial ênfase ao “Layout” e aos meios de remoção de passageiros do interior destas aeronaves.
3rd rowOrientar as suas organizações subordinadas em relação ao fiel cumprimento do estabelecido na ICA 100-37, de 28ABR2014, no seu item 5.9.3 e na MCA 100-16, de 18NOV2013, no item 2.3.3.
4th rowAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.
5th rowAtuar junto à Administração do Aeroporto Internacional de Guarulhos, de forma que este passe a ministrar treinamento teórico e prático de atendimento às vítimas de acidentes envolvendo os principais tipos de aeronaves que operam naquela localidade, principalmente os das Linhas Aéreas Regulares, com especial ênfase ao “Layout” e aos meios de remoção de passageiros do interior destas aeronaves.

Common Values

ValueCountFrequency (%)
Atuar junto à EJ Escola de Aeronáutica Ltda. ME e ao Aeroclube de Itápolis, a fim de que, conjuntamente, estas instituições realizem uma análise de risco sobre a realização de voos de instrução com aproximações de 180 e 360 graus concomitante a tráfegos executando circuitos normais (perna base e reta final) e “IFR simulados”, de maneira a facilitar a identificação dos perigos e a implementação de medidas mitigadoras adequadas.32
 
0.5%
Divulgar o conteúdo do presente relatório durante a realização de seminários, palestras e atividades afins voltadas aos proprietários, operadores e exploradores de aeronaves agrícolas.28
 
0.4%
Atuar junto a INFRAMERICA, a fim de que aquele Operador de Aeródromo adote medidas em relação à estrutura de iluminação dos pátios de estacionamento (Píer Norte e Píer Sul), de modo a evitar que a luz proveniente dos respectivos holofotes interfira negativamente na linha de visada dos controladores da TWR-BR.26
 
0.4%
ATUAR EM CONJUNTO COM A INFRAMERICA E O DECEA, A FIM DE QUE SEJA AVALIADA A PERTINêNCIA DE SE MODIFICAR UMA OU AMBAS AS LETRAS DESIGNATIVAS DAS TAXIWAYS “C” (CHARLIE) E “G” (GOLF) DO AERóDROMO INTERNACIONAL DE BRASíLIA, DE MODO A EVITAR CONFUSõES OU EQUíVOCOS POR PARTE DAS TRIPULAçõES QUE OPERAM NAQUELE AERóDROMO.26
 
0.4%
ATUAR EM CONJUNTO COM A INFRAMERICA E O DECEA, A FIM DE QUE CâMERAS DE USO EXCLUSIVO DO DTCEA-BR SEJAM INSTALADAS NO AERóDROMO INTERNACIONAL DE BRASíLIA, DE MODO A GARANTIR A VISUALIZAçãO E O CONTROLE DE TODOS OS PONTOS CEGOS DAQUELE AERóDROMO.26
 
0.4%
Atuar junto à LATAM AIRLINES GROUP S.A., a fim de que aquele operador reavalie a adequabilidade do programa de treinamento aplicado a seus pilotos, sobretudo no que diz respeito à frequência e ao controle dos treinamentos de pouso sem o Auto Thrust - Controle Automático de Empuxo (A/THR).26
 
0.4%
ANALISAR A PERTINêNCIA DE SE ESTABELECER UM MODELO OPERACIONAL QUE DEFINA, CLARAMENTE, OS PROCEDIMENTOS A SEREM ADOTADOS PELOS CONTROLADORES DE TRáFEGO AéREO DAS DIVERSAS POSIçõES DA TORRE DE CONTROLE DO AERóDROMO INTERNACIONAL DE BRASíLIA, EM RELAçãO à COORDENAçãO DOS TRáFEGOS QUANDO ESTES ESTIVEREM TRANSITANDO PELOS PONTOS CEGOS DO AERóDROMO.26
 
0.4%
Atuar junto ao pessoal dos Serviços de Tráfego Aéreo (ATS) no sentido de ratificar a observância dos padrões de fraseologia de tráfego aéreo constantes no MCA 100-16, atualmente em vigor, com o objetivo de assegurar a uniformidade das comunicações radiotelefônicas.26
 
0.4%
ANALISAR A PERTINêNCIA DE SE ESTABELECER, COM CLAREZA E EM NORMA, O MOMENTO OU A POSIçãO EM QUE, APóS O POUSO, A TRIPULAçãO DE UMA AERONAVE DEVE TROCAR A FREQUêNCIA DA TORRE DE CONTROLE PARA O CONTROLE DE SOLO.26
 
0.4%
ALERTAR OS CONTROLADORES DE TRáFEGO AéREO BRASILEIROS SOBRE A IMPORTâNCIA DA UTILIZAçãO DO RECURSO DE FRASEOLOGIA PREVISTO NO ITEM 6.14.6.4 DA ICA 100-37/2017, O QUAL ORIENTA QUE, QUANDO NECESSáRIO OU DESEJáVEL, O CONTROLADOR PODERá INSTRUIR UMA TRIPULAçãO A REPORTAR O MOMENTO EM QUE SUA RESPECTIVA AERONAVE TIVER DESOCUPADO A PISTA EM USO.26
 
0.4%
Other values (1107)6417
96.0%

Length

2022-05-29T10:28:51.326846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de26669
 
9.5%
a11595
 
4.1%
e7181
 
2.6%
que6447
 
2.3%
da5229
 
1.9%
do4974
 
1.8%
o4202
 
1.5%
os4200
 
1.5%
no3452
 
1.2%
à3346
 
1.2%
Other values (4673)203553
72.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recomendacao_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing28
Missing (%)0.4%
Memory size52.4 KiB
CUMPRIDA
4441 
AGUARDANDO RESPOSTA
1371 
CUMPRIDA DE FORMA ALTERNATIVA
463 
NÃO CUMPRIDA
 
384

Length

Max length29
Median length8
Mean length11.95554888
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCUMPRIDA
2nd rowCUMPRIDA
3rd rowAGUARDANDO RESPOSTA
4th rowCUMPRIDA
5th rowCUMPRIDA

Common Values

ValueCountFrequency (%)
CUMPRIDA4441
66.4%
AGUARDANDO RESPOSTA1371
 
20.5%
CUMPRIDA DE FORMA ALTERNATIVA463
 
6.9%
NÃO CUMPRIDA384
 
5.7%
(Missing)28
 
0.4%

Length

2022-05-29T10:28:51.431285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-29T10:28:51.486341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
cumprida5288
53.9%
aguardando1371
 
14.0%
resposta1371
 
14.0%
de463
 
4.7%
forma463
 
4.7%
alternativa463
 
4.7%
não384
 
3.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recomendacao_destinatario_sigla
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
ANAC
5949 
DECEA
 
581
ANP
 
29
SINDAG
 
21
CNP
 
20
Other values (13)
 
87

Length

Max length9
Median length4
Mean length4.113653357
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowANAC
2nd rowANAC
3rd rowDECEA
4th rowANAC
5th rowANAC

Common Values

ValueCountFrequency (%)
ANAC5949
89.0%
DECEA581
 
8.7%
ANP29
 
0.4%
SINDAG21
 
0.3%
CNP20
 
0.3%
SSP-GO20
 
0.3%
DIRSA16
 
0.2%
LYCOMING13
 
0.2%
AEROPREST12
 
0.2%
ABAG7
 
0.1%
Other values (8)19
 
0.3%

Length

2022-05-29T10:28:51.577135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
anac5949
89.0%
decea581
 
8.7%
anp29
 
0.4%
sindag21
 
0.3%
cnp20
 
0.3%
ssp-go20
 
0.3%
dirsa16
 
0.2%
lycoming13
 
0.2%
aeroprest12
 
0.2%
abag7
 
0.1%
Other values (8)19
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recomendacao_dias_para_feedback
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct246
Distinct (%)5.7%
Missing2338
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean173.8344447
Minimum0
Maximum1254
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2022-05-29T10:28:51.676327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q158
median108
Q3210
95-th percentile657
Maximum1254
Range1254
Interquartile range (IQR)152

Descriptive statistics

Standard deviation196.4224092
Coefficient of variation (CV)1.129939521
Kurtosis7.713546126
Mean173.8344447
Median Absolute Deviation (MAD)66
Skewness2.563101279
Sum756006
Variance38581.76283
MonotonicityNot monotonic
2022-05-29T10:28:51.791895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97177
 
2.6%
265142
 
2.1%
108112
 
1.7%
159108
 
1.6%
100102
 
1.5%
3090
 
1.3%
6184
 
1.3%
7982
 
1.2%
5677
 
1.2%
15872
 
1.1%
Other values (236)3303
49.4%
(Missing)2338
35.0%
ValueCountFrequency (%)
08
 
0.1%
38
 
0.1%
431
0.5%
518
 
0.3%
710
 
0.1%
833
0.5%
952
0.8%
1053
0.8%
1220
 
0.3%
1335
0.5%
ValueCountFrequency (%)
12548
 
0.1%
121920
0.3%
11483
 
< 0.1%
9381
 
< 0.1%
9372
 
< 0.1%
91540
0.6%
8942
 
< 0.1%
83210
 
0.1%
7867
 
0.1%
7675
 
0.1%

Interactions

2022-05-29T10:28:40.533566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:30.535386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:31.713385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:32.743081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:33.787586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:34.923922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:36.035457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:37.132609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:38.349151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:39.485023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:40.640206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:30.644506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:31.816057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:32.847272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:33.893235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:35.033566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:36.142593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:37.244208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:38.460255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:39.588688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:40.741390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:30.745194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:31.910297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:32.943395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:33.992435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:35.135218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:36.243777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:37.346415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:38.565904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:39.684910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:40.847533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:30.934666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:32.007513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:33.043587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:34.093123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:35.244338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:36.349452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:37.453372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:38.678992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:39.785102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:41.054861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:31.039818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:32.106713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:33.142787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:34.280114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:35.346514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:36.453613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:37.659184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:38.787615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:39.886782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:41.169934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:31.154394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:32.213353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:33.250419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:34.387250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:35.458114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:36.567169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:37.776240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:38.905695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:39.997886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:41.281038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:31.268970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:32.319497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:33.359539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:34.495378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:35.579138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:36.685217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:37.896800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:39.025755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:40.108990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:41.398122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:31.384041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:32.427129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:33.470147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:34.610946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:35.699665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:36.803265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:38.014352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:39.145262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:40.217614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:41.514157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:31.504073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:32.539721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:33.582739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:34.720562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:35.815233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:36.918833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:38.133391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:39.264302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:40.327230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:41.619805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:31.604265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:32.637433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:33.681939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:34.820754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:35.923361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:37.023985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:38.238543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:39.372431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-29T10:28:40.428414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-05-29T10:28:51.895559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-29T10:28:52.092966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-29T10:28:52.376183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-29T10:28:52.593926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-29T10:28:52.939142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-29T10:28:41.901533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-29T10:28:43.775119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-29T10:28:44.349297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-29T10:28:44.921681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ocorrencia_classificacaoocorrencia_cidadeocorrencia_ufocorrencia_aerodromoocorrencia_diaocorrencia_horainvestigacao_aeronave_liberadainvestigacao_statusdivulgacao_relatorio_publicadototal_recomendacoestotal_aeronaves_envolvidasocorrencia_saida_pistaocorrencia_localizacaoocorrencia_mesocorrencia_tipoocorrencia_tipo_categoriataxonomia_tipo_icaoaeronave_matriculaaeronave_operador_categoriaaeronave_tipo_veiculoaeronave_fabricanteaeronave_modeloaeronave_tipo_icaoaeronave_motor_tipoaeronave_motor_quantidadeaeronave_pmdaeronave_pmd_categoriaaeronave_assentosaeronave_ano_fabricacaoaeronave_registro_categoriaaeronave_registro_segmentoaeronave_voo_origemaeronave_voo_destinoaeronave_fase_operacaoaeronave_tipo_operacaoaeronave_nivel_danoaeronave_fatalidades_totalfator_nomefator_aspectofator_condicionantefator_arearecomendacao_conteudorecomendacao_statusrecomendacao_destinatario_siglarecomendacao_dias_para_feedback
0ACIDENTEGUARULHOS - SPSPSBGR1.013.0SIMFINALIZADASIM31NÃO-23.4355555556 / -46.47305555566.0COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKBNaNAVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0APLICAÇÃO DE COMANDOSDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.CUMPRIDAANAC117.0
1ACIDENTEGUARULHOS - SPSPSBGR1.013.0SIMFINALIZADASIM31NÃO-23.4355555556 / -46.47305555566.0COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKBNaNAVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0APLICAÇÃO DE COMANDOSDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à Administração do Aeroporto Internacional de Guarulhos, de forma que este passe a ministrar treinamento teórico e prático de atendimento às vítimas de acidentes envolvendo os principais tipos de aeronaves que operam naquela localidade, principalmente os das Linhas Aéreas Regulares, com especial ênfase ao “Layout” e aos meios de remoção de passageiros do interior destas aeronaves.CUMPRIDAANAC117.0
2ACIDENTEGUARULHOS - SPSPSBGR1.013.0SIMFINALIZADASIM31NÃO-23.4355555556 / -46.47305555566.0COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKBNaNAVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0APLICAÇÃO DE COMANDOSDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALOrientar as suas organizações subordinadas em relação ao fiel cumprimento do estabelecido na ICA 100-37, de 28ABR2014, no seu item 5.9.3 e na MCA 100-16, de 18NOV2013, no item 2.3.3.AGUARDANDO RESPOSTADECEANaN
3ACIDENTEGUARULHOS - SPSPSBGR1.013.0SIMFINALIZADASIM31NÃO-23.4355555556 / -46.47305555566.0COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKBNaNAVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0ATENÇÃOASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANOAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.CUMPRIDAANAC117.0
4ACIDENTEGUARULHOS - SPSPSBGR1.013.0SIMFINALIZADASIM31NÃO-23.4355555556 / -46.47305555566.0COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKBNaNAVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0ATENÇÃOASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANOAtuar junto à Administração do Aeroporto Internacional de Guarulhos, de forma que este passe a ministrar treinamento teórico e prático de atendimento às vítimas de acidentes envolvendo os principais tipos de aeronaves que operam naquela localidade, principalmente os das Linhas Aéreas Regulares, com especial ênfase ao “Layout” e aos meios de remoção de passageiros do interior destas aeronaves.CUMPRIDAANAC117.0
5ACIDENTEGUARULHOS - SPSPSBGR1.013.0SIMFINALIZADASIM31NÃO-23.4355555556 / -46.47305555566.0COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKBNaNAVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0ATENÇÃOASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANOOrientar as suas organizações subordinadas em relação ao fiel cumprimento do estabelecido na ICA 100-37, de 28ABR2014, no seu item 5.9.3 e na MCA 100-16, de 18NOV2013, no item 2.3.3.AGUARDANDO RESPOSTADECEANaN
6ACIDENTEGUARULHOS - SPSPSBGR1.013.0SIMFINALIZADASIM31NÃO-23.4355555556 / -46.47305555566.0COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKBNaNAVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0CAPACITAÇÃO E TREINAMENTOASPECTO PSICOLÓGICOORGANIZACIONALFATOR HUMANOAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.CUMPRIDAANAC117.0
7ACIDENTEGUARULHOS - SPSPSBGR1.013.0SIMFINALIZADASIM31NÃO-23.4355555556 / -46.47305555566.0COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKBNaNAVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0CAPACITAÇÃO E TREINAMENTOASPECTO PSICOLÓGICOORGANIZACIONALFATOR HUMANOAtuar junto à Administração do Aeroporto Internacional de Guarulhos, de forma que este passe a ministrar treinamento teórico e prático de atendimento às vítimas de acidentes envolvendo os principais tipos de aeronaves que operam naquela localidade, principalmente os das Linhas Aéreas Regulares, com especial ênfase ao “Layout” e aos meios de remoção de passageiros do interior destas aeronaves.CUMPRIDAANAC117.0
8ACIDENTEGUARULHOS - SPSPSBGR1.013.0SIMFINALIZADASIM31NÃO-23.4355555556 / -46.47305555566.0COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKBNaNAVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0CAPACITAÇÃO E TREINAMENTOASPECTO PSICOLÓGICOORGANIZACIONALFATOR HUMANOOrientar as suas organizações subordinadas em relação ao fiel cumprimento do estabelecido na ICA 100-37, de 28ABR2014, no seu item 5.9.3 e na MCA 100-16, de 18NOV2013, no item 2.3.3.AGUARDANDO RESPOSTADECEANaN
9ACIDENTEGUARULHOS - SPSPSBGR1.013.0SIMFINALIZADASIM31NÃO-23.4355555556 / -46.47305555566.0COM PESSOAL EM VOOOUTROS | COM PESSOAL EM VOOOTHRPRTKBNaNAVIÃOAEROSPATIALE AND ALENIAATR-42-500AT45TURBOÉLICEBIMOTOR186001860050.02001.0AVIÃOREGULARFORA DE AERODROMOFORA DE AERODROMODESCIDAREGULARNENHUM0CLIMA ORGANIZACIONALASPECTO PSICOLÓGICOORGANIZACIONALFATOR HUMANOAtuar junto às empresas que operam segundo o RBAC 121 de forma tal que assegure que o treinamento de Corporate Ressource Management (CRM) esteja adequado a realidade daquela empresa, que seja constantemente avaliado e reforçado (com a participação da alta direção), envolva todos os profissionais da instituição, que garanta a integração dos diferentes setores da empresa (corporate), e que, acima de tudo, faça parte da cultura de segurança de voo da organização.CUMPRIDAANAC117.0

Last rows

ocorrencia_classificacaoocorrencia_cidadeocorrencia_ufocorrencia_aerodromoocorrencia_diaocorrencia_horainvestigacao_aeronave_liberadainvestigacao_statusdivulgacao_relatorio_publicadototal_recomendacoestotal_aeronaves_envolvidasocorrencia_saida_pistaocorrencia_localizacaoocorrencia_mesocorrencia_tipoocorrencia_tipo_categoriataxonomia_tipo_icaoaeronave_matriculaaeronave_operador_categoriaaeronave_tipo_veiculoaeronave_fabricanteaeronave_modeloaeronave_tipo_icaoaeronave_motor_tipoaeronave_motor_quantidadeaeronave_pmdaeronave_pmd_categoriaaeronave_assentosaeronave_ano_fabricacaoaeronave_registro_categoriaaeronave_registro_segmentoaeronave_voo_origemaeronave_voo_destinoaeronave_fase_operacaoaeronave_tipo_operacaoaeronave_nivel_danoaeronave_fatalidades_totalfator_nomefator_aspectofator_condicionantefator_arearecomendacao_conteudorecomendacao_statusrecomendacao_destinatario_siglarecomendacao_dias_para_feedback
6677ACIDENTESANTA VITÓRIA DO PALMAR - RSRSNaN12.013.0SIMFINALIZADASIM31NÃO\t-33.176944\t / \t-53.014167\t6.0PANE SECACOMBUSTÍVEL | PANE SECAFUELPTUXDNaNAVIÃOEMBRAEREMB-202IPANPISTÃOMONOMOTOR180018001.02009.0AVIÃOAGRÍCOLAPISTA PLÁ E SILVAPISTA PLÁ E SILVAMANOBRAAGRÍCOLASUBSTANCIAL0MEMÓRIAASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANOAtuar junto à empresa Plá e Silva Aviação Agrícola Ltda., no intuito de que aquele operador oriente seus tripulantes a manterem a bomba elétrica auxiliar de combustível ligada, conforme prevê o Boletim de Informação (BI) 200-028-0022, emitido pela Embraer.AGUARDANDO RESPOSTAANACNaN
6678ACIDENTESANTA VITÓRIA DO PALMAR - RSRSNaN12.013.0SIMFINALIZADASIM31NÃO\t-33.176944\t / \t-53.014167\t6.0PANE SECACOMBUSTÍVEL | PANE SECAFUELPTUXDNaNAVIÃOEMBRAEREMB-202IPANPISTÃOMONOMOTOR180018001.02009.0AVIÃOAGRÍCOLAPISTA PLÁ E SILVAPISTA PLÁ E SILVAMANOBRAAGRÍCOLASUBSTANCIAL0MEMÓRIAASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANODivulgar os ensinamentos colhidos na presente investigação, com o propósito de alertar pilotos e operadores das aeronaves EMB-200, EMB-200A, EMB-201, EMB-201A, EMB-202, EMB-202A e EMB-203 sobre a importância de se manter a bomba elétrica auxiliar de combustível ligada, conforme prevê o Boletim de Informação (BI) 200-028-0022, emitido pela Embraer.AGUARDANDO RESPOSTAANACNaN
6679ACIDENTESANTA VITÓRIA DO PALMAR - RSRSNaN12.013.0SIMFINALIZADASIM31NÃO\t-33.176944\t / \t-53.014167\t6.0PANE SECACOMBUSTÍVEL | PANE SECAFUELPTUXDNaNAVIÃOEMBRAEREMB-202IPANPISTÃOMONOMOTOR180018001.02009.0AVIÃOAGRÍCOLAPISTA PLÁ E SILVAPISTA PLÁ E SILVAMANOBRAAGRÍCOLASUBSTANCIAL0PROCESSO DECISÓRIOASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANOAtuar junto à empresa Plá e Silva Aviação Agrícola Ltda., a fim de que aquele operador aprimore suas atividades de supervisão gerencial e que padronize seus procedimentos operacionais, como forma de estabelecer barreiras eficazes ao erro humano.AGUARDANDO RESPOSTAANACNaN
6680ACIDENTESANTA VITÓRIA DO PALMAR - RSRSNaN12.013.0SIMFINALIZADASIM31NÃO\t-33.176944\t / \t-53.014167\t6.0PANE SECACOMBUSTÍVEL | PANE SECAFUELPTUXDNaNAVIÃOEMBRAEREMB-202IPANPISTÃOMONOMOTOR180018001.02009.0AVIÃOAGRÍCOLAPISTA PLÁ E SILVAPISTA PLÁ E SILVAMANOBRAAGRÍCOLASUBSTANCIAL0PROCESSO DECISÓRIOASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANOAtuar junto à empresa Plá e Silva Aviação Agrícola Ltda., no intuito de que aquele operador oriente seus tripulantes a manterem a bomba elétrica auxiliar de combustível ligada, conforme prevê o Boletim de Informação (BI) 200-028-0022, emitido pela Embraer.AGUARDANDO RESPOSTAANACNaN
6681ACIDENTESANTA VITÓRIA DO PALMAR - RSRSNaN12.013.0SIMFINALIZADASIM31NÃO\t-33.176944\t / \t-53.014167\t6.0PANE SECACOMBUSTÍVEL | PANE SECAFUELPTUXDNaNAVIÃOEMBRAEREMB-202IPANPISTÃOMONOMOTOR180018001.02009.0AVIÃOAGRÍCOLAPISTA PLÁ E SILVAPISTA PLÁ E SILVAMANOBRAAGRÍCOLASUBSTANCIAL0PROCESSO DECISÓRIOASPECTO PSICOLÓGICOINDIVIDUALFATOR HUMANODivulgar os ensinamentos colhidos na presente investigação, com o propósito de alertar pilotos e operadores das aeronaves EMB-200, EMB-200A, EMB-201, EMB-201A, EMB-202, EMB-202A e EMB-203 sobre a importância de se manter a bomba elétrica auxiliar de combustível ligada, conforme prevê o Boletim de Informação (BI) 200-028-0022, emitido pela Embraer.AGUARDANDO RESPOSTAANACNaN
6682ACIDENTESANTA VITÓRIA DO PALMAR - RSRSNaN12.013.0SIMFINALIZADASIM31NÃO\t-33.176944\t / \t-53.014167\t6.0PANE SECACOMBUSTÍVEL | PANE SECAFUELPTUXDNaNAVIÃOEMBRAEREMB-202IPANPISTÃOMONOMOTOR180018001.02009.0AVIÃOAGRÍCOLAPISTA PLÁ E SILVAPISTA PLÁ E SILVAMANOBRAAGRÍCOLASUBSTANCIAL0SUPERVISÃO GERENCIALDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à empresa Plá e Silva Aviação Agrícola Ltda., a fim de que aquele operador aprimore suas atividades de supervisão gerencial e que padronize seus procedimentos operacionais, como forma de estabelecer barreiras eficazes ao erro humano.AGUARDANDO RESPOSTAANACNaN
6683ACIDENTESANTA VITÓRIA DO PALMAR - RSRSNaN12.013.0SIMFINALIZADASIM31NÃO\t-33.176944\t / \t-53.014167\t6.0PANE SECACOMBUSTÍVEL | PANE SECAFUELPTUXDNaNAVIÃOEMBRAEREMB-202IPANPISTÃOMONOMOTOR180018001.02009.0AVIÃOAGRÍCOLAPISTA PLÁ E SILVAPISTA PLÁ E SILVAMANOBRAAGRÍCOLASUBSTANCIAL0SUPERVISÃO GERENCIALDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALAtuar junto à empresa Plá e Silva Aviação Agrícola Ltda., no intuito de que aquele operador oriente seus tripulantes a manterem a bomba elétrica auxiliar de combustível ligada, conforme prevê o Boletim de Informação (BI) 200-028-0022, emitido pela Embraer.AGUARDANDO RESPOSTAANACNaN
6684ACIDENTESANTA VITÓRIA DO PALMAR - RSRSNaN12.013.0SIMFINALIZADASIM31NÃO\t-33.176944\t / \t-53.014167\t6.0PANE SECACOMBUSTÍVEL | PANE SECAFUELPTUXDNaNAVIÃOEMBRAEREMB-202IPANPISTÃOMONOMOTOR180018001.02009.0AVIÃOAGRÍCOLAPISTA PLÁ E SILVAPISTA PLÁ E SILVAMANOBRAAGRÍCOLASUBSTANCIAL0SUPERVISÃO GERENCIALDESEMPENHO DO SER HUMANOOPERAÇÃO DA AERONAVEFATOR OPERACIONALDivulgar os ensinamentos colhidos na presente investigação, com o propósito de alertar pilotos e operadores das aeronaves EMB-200, EMB-200A, EMB-201, EMB-201A, EMB-202, EMB-202A e EMB-203 sobre a importância de se manter a bomba elétrica auxiliar de combustível ligada, conforme prevê o Boletim de Informação (BI) 200-028-0022, emitido pela Embraer.AGUARDANDO RESPOSTAANACNaN
6685ACIDENTEARACAJU - SESENaN5.014.0SIMFINALIZADASIM21NÃO-10.98472 / -37.051666.0PERDA DE CONTROLE EM VOOPERDA DE CONTROLE EM VOOLOC-IPRZSFNaNNaNNaNNaNNaNNaNSEM TRAÇÃO000.0NaNNaNEXPERIMENTALSANTA MARIAFAZENDA SANTO ANTÔNIORETA FINALPRIVADADESTRUÍDA1MANUTENÇÃO DA AERONAVEDESEMPENHO DO SER HUMANOMANUTENÇÃO DA AERONAVEFATOR OPERACIONALDivulgar os ensinamentos colhidos nesta investigação aos operadores de aeronaves experimentais, modelos RV-10 e RV-10A, alertando-os para a necessidade de se dispensar especial atenção para o estado geral das cablagens do atuador dos compensadores, por ocasião da realização dos procedimentos de manutenção preventiva, visando identificar condições indesejáveis que possam concorrer para um contato acidental dos cabos condutores com a estrutura do manche, resultando no acionamento involuntário do relé do trim.AGUARDANDO RESPOSTAANACNaN
6686ACIDENTEARACAJU - SESENaN5.014.0SIMFINALIZADASIM21NÃO-10.98472 / -37.051666.0PERDA DE CONTROLE EM VOOPERDA DE CONTROLE EM VOOLOC-IPRZSFNaNNaNNaNNaNNaNNaNSEM TRAÇÃO000.0NaNNaNEXPERIMENTALSANTA MARIAFAZENDA SANTO ANTÔNIORETA FINALPRIVADADESTRUÍDA1MANUTENÇÃO DA AERONAVEDESEMPENHO DO SER HUMANOMANUTENÇÃO DA AERONAVEFATOR OPERACIONALDivulgar os ensinamentos colhidos nesta investigação aos operadores de aeronaves experimentais, modelos RV-10 e RV-10A, afetadas pelo Boletim de Serviço, SB-001/17-Flyer, alertando-os para o fiel cumprimento do referido boletim.AGUARDANDO RESPOSTAANACNaN